Zhu2019_DataAnalysis

IMPORT LIBRAIRIES

IMPORT DATA

# Import metadata
metadata_table <- read.table("/Users/enigma/Desktop/Munich/Praktikum/Data/Zhu_2019/ZhuSraRunTable.txt", header = TRUE, sep = ",")
metadata_table <- metadata_table[, c("Run", "Age", "Sample.Name", "sex")]# Keep relevant data
rownames(metadata_table) <- paste(metadata_table$Run) # have the same sample.names
metadata_table$Sample.Name <- as.character(metadata_table$Sample.Name)
colnames(metadata_table) <- c("Run", "age", "host_disease", "gender")
metadata_table$host_disease[substr(metadata_table$host_disease, start = 1, stop = 2) == "N_"] <- "Healthy" # Replace with better names
metadata_table$host_disease[substr(metadata_table$host_disease, start = 1, stop = 2) == "F_"] <- "IBS" # Replace with better names
table(metadata_table$host_disease)

# Import OTU table (rows: sample names // columns: sequence variants)
seqtable.nochim <- readRDS("/Users/enigma/Desktop/Munich/Praktikum/Data/Zhu_2019/ASVtable_final.rds")
dim(seqtable.nochim) # should have 29 samples and 652 ASV


# Import Taxonomic table (rows: sequence variants // columns: Kingdom, Phylum, Class, ...)
taxa <- readRDS("/Users/enigma/Desktop/Munich/Praktikum/Data/Zhu_2019/taxa_final.rds")
dim(taxa)

# Remove unknown eukaryota
seqtable.nochim <- seqtable.nochim[ , !(colnames(seqtable.nochim) %in% rownames(taxa[taxa[, 'Kingdom'] == 'Eukaryota',])) ]
taxa <- taxa[taxa[, 'Kingdom'] == 'Bacteria',] # keep only bacteria

dim(seqtable.nochim) # sanity check 648 OTUs
dim(taxa)

CREATE PHYLOSEQ OBJECT

#____________________________________________________________________
# OTU TABLE, TAXA TABLE AND METADATA
physeq <- phyloseq(otu_table(seqtable.nochim, taxa_are_rows=FALSE), # by default, in otu_table the sequence variants are in rows
                  sample_data(metadata_table), 
                  tax_table(taxa))

#____________________________________________________________________
# PHYLOGENETIC TREE

# Multiple-sequence alignment (know which regions are conserved/different to be able to do the phylogeny)
seqs <- getSequences(seqtable.nochim) # get the sequence variants (the ASVs)
names(seqs) <- seqs # This propagates to the tip labels of the tree
alignment <- AlignSeqs(DNAStringSet(seqs), anchor=NA) # by default, anchor = 0.7 which means that 70% of sequences must share a common region to anchor the alignment space.

# Construct a neighbor-joining tree
phang.align <- phyDat(as(alignment, "matrix"), type="DNA") # transform the aligned DNA sequences into a phyDat (phangorn) object
dm <- dist.ml(phang.align) # compute pairwise distance between DNA sequences (with the Jukes-Cantor estimate of the evolutionary distance)
treeNJ <- NJ(dm) # create a neighbor-joining tree estimation based on the distance matrix
fit <- pml(treeNJ, data=phang.align) # get the likelihood of the phylogenetic tree given the sequence alignment (then we'll optimize it)

## negative edges length changed to 0!

# Fit a generalized time-reversible with gamma rate variation (GTR+G+I)
fitGTR <- update(fit, k=4, inv=0.2)
fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE, # gamma rate and proportion of variable size get optimized
                      rearrangement = "stochastic", control = pml.control(trace = 0)) # (trace = 0) don't show output during optimization
# stochastic tree rearrangement

# Add tree to physeq
physeq <- merge_phyloseq(physeq, phy_tree(fitGTR$tree))

# Look at the tree
plot_tree(physeq, color = "host_disease", ladderize="left")

#____________________________________________________________________
# GIVE SURNAMES TO OTUs

# Give surnames to sequence variants & store the sequence variants in "refseq" in the phyloseq object
dna <- DNAStringSet(taxa_names(physeq)) # get the sequence variants (ASVs)
names(dna) <- taxa_names(physeq) # no idea what this does
physeq <- merge_phyloseq(physeq, dna) # store the dna sequences in the refseq of the phyloseq object
taxa_names(physeq) <- paste0("OTU", seq(ntaxa(physeq))) # replace the whole dna sequences in the taxa_names by a surname OTU1, OTU2, etc.

# Save physeq object
saveRDS(physeq, "/Users/enigma/Desktop/Munich/Praktikum/Data/Zhu_2019/Rproject_DataAnalysis/physeq.rds")

ABSOLUTE AND RELATIVE ABUNDANCES

# Relative abundance for Phylum
phylum.table <- physeq %>%
  tax_glom(taxrank = "Phylum") %>%                     # agglomerate at phylum level
  transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
  psmelt()                                             # Melt to long format

# Relative abundance for Class
class.table <- physeq %>%
  tax_glom(taxrank = "Class") %>%                     # agglomerate at class level
  transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
  psmelt()                                             # Melt to long format


# Plot Phylum
plot_bar(physeq, fill = "Phylum") + facet_wrap("host_disease", scales="free") +
  theme(axis.text.x = element_text(size = 8))+
  labs(x = "Samples", y = "Total read count", title = "Zhu dataset (2019)")+
  ylim(0, 17000)

ggplot(phylum.table, aes(x = Sample, y = Abundance, fill = Phylum))+
  facet_wrap(~ host_disease, scales = "free") + # scales = "free" removes empty lines
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(size = 8, angle = -90))+
  labs(x = "Samples", y = "Relative abundance", title = "Zhu dataset (2019)")

# Plot Class
plot_bar(physeq, fill = "Class")+ facet_wrap("host_disease", scales="free") +
  theme(axis.text.x = element_text(size = 8))+
  labs(x = "Samples", y = "Total read count", title = "Zhu dataset (2019)")+
  ylim(0, 17000)

ggplot(class.table, aes(x = Sample, y = Abundance, fill = Class))+
  facet_wrap(~ host_disease, scales = "free") + # scales = "free" removes empty lines
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(size = 8, angle = -90))+
  labs(x = "Samples", y = "Relative abundance", title = "Zhu dataset (2019)")

# Extract abundance of only Bacteroidota and Firmicutes
bacter <- phylum.table[phylum.table[,'Phylum'] == 'Bacteroidota', c('Sample', 'Abundance',
                                                                    'host_disease', 'gender', 'Phylum')]
bacter <- bacter[order(bacter$Sample),] # order by sample name
firmi <- phylum.table[phylum.table[,'Phylum'] == 'Firmicutes', c('Sample', 'Abundance',
                                                                 'host_disease', 'gender', 'Phylum')]
firmi <- firmi[order(firmi$Sample),] # order by sample name

# Calculate log2 ratio Firmicutes/Bacteroidota
ratio_FB <- data.table('Sample' = bacter$Sample,
                       'host_disease' = bacter$host_disease,
                       'gender' = bacter$gender,
                       'Bacteroidota' = bacter$Abundance,
                       'Firmicutes' = firmi$Abundance)
ratio_FB[,"Log_ratio_FB"] <- log2(ratio_FB[,"Firmicutes"] / ratio_FB[,"Bacteroidota"])

# Plot log2 ratio Firmicutes/Bacteroidota
par(mar = c(3, 10, 3, 10))
boxplot(data = ratio_FB, Log_ratio_FB ~ host_disease, ylab = "Log2 Ratio Abundance", xlab = "", main = "Firmicutes to Bacteroidota ratio")

par(mar = c(3, 10, 3, 10))
boxplot(data = ratio_FB, Log_ratio_FB ~ gender, ylab = "Log2 Ratio Abundance", xlab = "", main = "Firmicutes to Bacteroidota ratio")

NORMALIZE DATA

#____________________________________________________________________
# PHYLOSEQ OBJECT WITH PSEUDOCOUNTS
physeq.pseudocts <- physeq
otu_table(physeq.pseudocts)[otu_table(physeq.pseudocts) == 0] <- 0.5

# check the 0 values have been replaced
otu_table(physeq)[1:5,1:5]
otu_table(physeq.pseudocts)[1:5,1:5]

# save the physeq.pseudocts object
saveRDS(physeq.pseudocts, "/Users/enigma/Desktop/Munich/Praktikum/Data/Zhu_2019/Rproject_DataAnalysis/physeq_pseudocts.rds")


#____________________________________________________________________
# PHYLOSEQ OBJECT WITH RELATIVE COUNT (BETWEEN 0 AND 1)
physeq.rel <- physeq.pseudocts
physeq.rel <- transform_sample_counts(physeq.rel, function(x) x / sum(x) ) # divide each count by the total number of counts (per sample)

# check the counts are all relative
otu_table(physeq.pseudocts)[1:5, 1:5]
otu_table(physeq.rel)[1:5, 1:5]

# sanity check
sum(otu_table(physeq.rel) < 0) # see how many negative values are present in the matrix
sum(rowSums(otu_table(physeq.rel)) == 1) # check if there is any row not summing to 1

# save the physeq.rel object
saveRDS(physeq.rel, "/Users/enigma/Desktop/Munich/Praktikum/Data/Zhu_2019/Rproject_DataAnalysis/physeq_relative.rds")


#____________________________________________________________________
# PHYLOSEQ OBJECT WITH CENTERED LOG RATIO COUNT
physeq.clr <- physeq.pseudocts
physeq.clr <- transform(physeq.clr, "clr")

# Compare the otu tables in the original phyloseq object and the new one after CLR transformation
otu_table(physeq.pseudocts)[1:5, 1:5] # should contain absolute counts
otu_table(physeq.clr)[1:5, 1:5] # should all be relative

# save the physeq.rel object
saveRDS(physeq.clr, "/Users/enigma/Desktop/Munich/Praktikum/Data/Zhu_2019/Rproject_DataAnalysis/physeq_clr.rds")

COMPUTE DISTANCES

#____________________________________________________________________________________
# Measure distances
Distances <- function(physeq_obj){
  set.seed(123) # for unifrac, need to set a seed
  glom.UniF <-  UniFrac(physeq_obj, weighted=TRUE, normalized=TRUE) # weighted unifrac
  glom.ait <- distance(subset_samples(physeq.clr, sample_names(physeq.clr) != 'SRR5245605'), method = 'euclidean') # aitchison
  glom.bray <- distance(physeq_obj, method = "bray") # bray-curtis
  glom.jac <- distance(physeq_obj, method = "jaccard") # jaccard
  glom.can <- distance(physeq_obj, method = "canberra") # canberra
  glom.bin <- distance(physeq_obj, method = "binomial") # binomial
  dist.list <- list("UniF" = glom.UniF, "Ait" = glom.ait, "Canb" = glom.can, "Bray" = glom.bray, "Jac" = glom.jac, "Bin" = glom.bin)
  
  return(dist.list)
}


#____________________________________________________________________________________
# Plot 2D ordination
MDS_2D <- function(physeq_obj, ait_dist){
  
  plist <- NULL
  plist <- vector("list", length(dist_methods)+1) # save each plot to a list
  names(plist) <- dist_methods # save the name of each method
  names(plist)[17] <- "aitchison" # save the name of aitchison
  
  # Loop through all distance methods
  for(i in dist_methods){
    # Calculate distance matrix
    #print(i)
    set.seed(123) # in case the distance method needs a rooted tree (weighted unifrac)
    iDist <- phyloseq::distance(physeq_obj, method=i)
    # Calculate ordination
    set.seed(123)
    iMDS  <- ordinate(physeq_obj, "MDS", distance=iDist)
    ## Make plot
    # Don't carry over previous plot (if error, p will be blank)
    p <- NULL
    # Create plot, store as temporary variable, p
    p <- plot_ordination(physeq_obj, iMDS, color="host_disease")
    # Add title to each plot
    p <- p + ggtitle(paste("MDS using distance method ", i, sep=""))
    # Save the graphic to the plot list
    plist[[i]] = p
  }
  
  # Add aitchison
  iMDS  <- ordinate(physeq_obj, "MDS", distance=ait_dist)
  p <- NULL
  p <- plot_ordination(physeq_obj, iMDS, color="host_disease")
  p <- p + ggtitle("MDS using distance method Aitchison")
  plist[[17]] = p
  
  # Creating a dataframe to plot everything
  plot.df = ldply(plist, function(x) x$data)
  names(plot.df)[1] <- "distance"
  
  # Plot
  p.alldist <-  ggplot(plot.df, aes(Axis.1, Axis.2, color=host_disease))+
    geom_point(size=5, alpha=0.5)  + scale_color_manual(values = c('blue', 'red'))+
    facet_wrap(~distance, scales='free')+
    theme(strip.text = element_text(size = 40),
          legend.text = element_text(size = 20),
          axis.text.x = element_text(size = 20),
          axis.text.y = element_text(size = 20))

  return(p.alldist)
}

#____________________________________________________________________________________
# Plot 3D ordination
MDS_3D <- function(d, name_dist){
  
  # Reset parameters
  mds.3D <- NULL
  xyz <- NULL
  fig.3D <- NULL
  
  # Reduce distance matrix to 3 dimensions
  set.seed(123) # to get the same dimensionality reduction at each run
  mds.3D <- metaMDS(d, method="MDS", k=3, trace = 0)
  xyz <- scores(mds.3D, display="sites") # pull out the x y z coordinates
  
  if(name_dist == 'Aitchison'){
    fig.3D <- plot_ly(x=xyz[,1], y=xyz[,2], z=xyz[,3], type='scatter3d', mode='markers',
                      color=sample_data(subset_samples(physeq.rel, sample_names(physeq.rel) != 'SRR5245605'))$host_disease,
                      colors = c('blue', 'red')) %>%
    layout(title = paste('MDS in 3D with', name_dist, 'distance', sep = ' '))
  }
  
  else{
    fig.3D <- plot_ly(x=xyz[,1], y=xyz[,2], z=xyz[,3], type='scatter3d',
                       mode='markers', color=sample_data(physeq.rel)$host_disease, colors = c('blue', 'red')) %>%
      #add_trace(x=xyz['SRR5245605',1], y=xyz['SRR5245605',2], z=xyz['SRR5245605',3],
      #      type='scatter3d', mode='text', text = 'SRR5245605', textfont = list(color = 'blue')) %>%
      layout(title = paste('MDS in 3D with', name_dist, 'distance', sep = ' '))
  }
  
  return(fig.3D)
}

# Get the distances
no_glom.dist <- Distances(physeq_obj = physeq.rel)

# Get the 2D ordination plots
no_glom.2D.alldist <- MDS_2D(physeq.rel, no_glom.dist$Ait)
no_glom.2D.alldist + theme(title = element_text(size = 30))

# Get 3D MDS plots
no_glom.3D.UniF <- MDS_3D(no_glom.dist$UniF, 'weighted Unifrac')
no_glom.3D.Ait <- MDS_3D(no_glom.dist$Ait, 'Aitchison')
no_glom.3D.Jac <- MDS_3D(no_glom.dist$Jac, 'Jaccard')
no_glom.3D.Can <- MDS_3D(no_glom.dist$Canb, 'Canberra')
no_glom.3D.Bin <- MDS_3D(no_glom.dist$Bin, 'Binomial')

no_glom.3D.UniF

no_glom.3D.Ait

no_glom.3D.Jac

no_glom.3D.Can

no_glom.3D.Bin

HIERARCHICAL CLUSTERING

Heatmaps <- function(dist_list, fontsize){
  # Weighted Unifrac
  heatmp.UniF <- pheatmap(as.matrix(dist_list$UniF), 
                          clustering_distance_rows = dist_list$UniF,
                          clustering_distance_cols = dist_list$UniF,
                          fontsize = fontsize,
                          fontsize_col = fontsize-5,
                          fontsize_row = fontsize-5,
                          annotation_col = mat_col,
                          annotation_row = mat_col,
                          annotation_colors = list(group = c('Healthy' = 'blue', 'IBS' = 'red')),
                          cluster_rows = T,
                          cluster_cols = T,
                          clustering_method = 'complete', #hierarchical method
                          main = 'Weighted unifrac distance')

  # Aitchison
  heatmp.Ait <- pheatmap(as.matrix(dist_list$Ait), 
                         clustering_distance_rows = dist_list$Ait,
                         clustering_distance_cols = dist_list$Ait,
                         fontsize = fontsize,
                         fontsize_col = fontsize-5,
                         fontsize_row = fontsize-5,
                         # border_color = NA,
                         cluster_rows = T,
                         cluster_cols = T,
                         clustering_method = "complete", #hierarchical method
                         annotation_col = mat_col,
                         annotation_row = mat_col,
                         annotation_colors = list(group = c('Healthy' = 'blue', 'IBS' = 'red')),
                         main = "Aitchison distance")
  
  # Bray-Curtis
  heatmp.Bray <- pheatmap(as.matrix(dist_list$Bray), 
                         clustering_distance_rows = dist_list$Bray,
                         clustering_distance_cols = dist_list$Bray,
                         fontsize = fontsize,
                         fontsize_col = fontsize-5,
                         fontsize_row = fontsize-5,
                         # border_color = NA,
                         cluster_rows = T,
                         cluster_cols = T,
                         clustering_method = "complete", #hierarchical method
                         annotation_col = mat_col,
                         annotation_row = mat_col,
                         annotation_colors = list(group = c('Healthy' = 'blue', 'IBS' = 'red')),
                         main = "Bray-Curtis distance")
  
  # Jaccard
  heatmp.Jac <- pheatmap(as.matrix(dist_list$Jac), 
                         clustering_distance_rows = dist_list$Jac,
                         clustering_distance_cols = dist_list$Jac,
                         fontsize = fontsize,
                         fontsize_col = fontsize-5,
                         fontsize_row = fontsize-5,
                         # border_color = NA,
                         cluster_rows = T,
                         cluster_cols = T,
                         clustering_method = "complete", #hierarchical method
                         annotation_col = mat_col,
                         annotation_row = mat_col,
                         annotation_colors = list(group = c('Healthy' = 'blue', 'IBS' = 'red')),
                         main = "Jaccard distance")
  
  # Binomial
  heatmp.Bin <- pheatmap(as.matrix(dist_list$Bin), 
                         clustering_distance_rows = dist_list$Bin,
                         clustering_distance_cols = dist_list$Bin,
                         fontsize = fontsize,
                         fontsize_col = fontsize-5,
                         fontsize_row = fontsize-5,
                         # border_color = NA,
                         cluster_rows = T,
                         cluster_cols = T,
                         clustering_method = "complete", #hierarchical method
                         annotation_col = mat_col,
                         annotation_row = mat_col,
                         annotation_colors = list(group = c('Healthy' = 'blue', 'IBS' = 'red')),
                         main = "Binomial distance")
  
  # Canberra
  heatmp.Canb <- pheatmap(as.matrix(dist_list$Canb), 
                         clustering_distance_rows = dist_list$Canb,
                         clustering_distance_cols = dist_list$Canb,
                         fontsize = fontsize,
                         fontsize_col = fontsize-5,
                         fontsize_row = fontsize-5,
                         # border_color = NA,
                         cluster_rows = T,
                         cluster_cols = T,
                         clustering_method = "complete", #hierarchical method
                         annotation_col = mat_col,
                         annotation_row = mat_col,
                         annotation_colors = list(group = c('Healthy' = 'blue', 'IBS' = 'red')),
                         main = "Canberra distance")
  
  return(list("UniF" = heatmp.UniF, "Ait" = heatmp.Ait, "Bray" = heatmp.Bray, "Jac" = heatmp.Jac, "Bin" = heatmp.Bin,
              "Canb" = heatmp.Canb))
}


# Get the heatmaps
no_glom.heatmaps <- Heatmaps(dist_list = no_glom.dist, fontsize = 8)